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AUTOMATIC1111 / stable-diffusion-webui

Stable Diffusion web UI
GNU Affero General Public License v3.0
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[Bug]: dreambooth plugin crashes while creating model using SDXL1.0 source #12147

Closed JokerGT closed 1 year ago

JokerGT commented 1 year ago

Is there an existing issue for this?

What happened?

``my installed version version: [v1.5.1-55-g25004d4e] •  python: 3.10.9  •  torch: 2.0.1+cu118  •  xformers: 0.0.20  •  gradio: 3.32.0  •  i installed dreambooth plugin and tried to create a new model using the SDXL1.0 source but it failed:

Steps to reproduce the problem

  1. Go to ....
  2. Press ....
  3. ...

What should have happened?

the model should have been created like it did with sd1-5

Version or Commit where the problem happens

[v1.5.1-55-g25004d4e]

What Python version are you running on ?

None

What platforms do you use to access the UI ?

No response

What device are you running WebUI on?

No response

Cross attention optimization

Automatic

What browsers do you use to access the UI ?

No response

Command Line Arguments

--xformers

List of extensions

only dreambooth - everything else standard installation

Console logs

C:\Users\User>cd stable-diffusion-webui
C:\Users\User\stable-diffusion-webui>webui-user.bat
Already up to date.
venv "C:\Users\User\stable-diffusion-webui\venv\Scripts\Python.exe"
Python 3.10.9 (tags/v3.10.9:1dd9be6, Dec  6 2022, 20:01:21) [MSC v.1934 64 bit (AMD64)]
Version: v1.5.1-55-g25004d4e
Commit hash: 25004d4eeef015d8f886c537d3a5a9f54d07898e
Installing requirements
If submitting an issue on github, please provide the full startup log for debugging purposes.

Initializing Dreambooth
Dreambooth revision: c2a5617c587b812b5a408143ddfb18fc49234edf
Successfully installed accelerate-0.19.0 fastapi-0.94.1 gitpython-3.1.32 transformers-4.30.2

Does your project take forever to startup?
Repetitive dependency installation may be the reason.
AuUseratic1111's base project sets strict requirements on outdated dependencies.
If an extension is using a newer version, the dependency is uninstalled and reinstalled twice every startup.

[+] xformers version 0.0.20 installed.
[+] torch version 2.0.1+cu118 installed.
[+] torchvision version 0.15.2+cu118 installed.
[+] accelerate version 0.19.0 installed.
[+] diffusers version 0.16.1 installed.
[+] transformers version 4.30.2 installed.
[+] bitsandbytes version 0.35.4 installed.

Launching Web UI with arguments: --xformers
Loading weights [31e35c80fc] from C:\Users\User\stable-diffusion-webui\models\Stable-diffusion\sd_xl_base_1.0.safetensors
Creating model from config: C:\Users\User\stable-diffusion-webui\repositories\generative-models\configs\inference\sd_xl_base.yaml
Model loaded in 6.5s (load weights from disk: 0.5s, create model: 0.4s, apply weights to model: 1.3s, apply half(): 1.6s, move model to device: 1.2s, load textual inversion embeddings: 0.8s, calculate empty prompt: 0.7s).
Applying attention optimization: xformers... done.
CUDA SETUP: Loading binary C:\Users\User\stable-diffusion-webui\venv\lib\site-packages\bitsandbytes\libbitsandbytes_cudaall.dll...
Running on local URL:  http://127.0.0.1:7860

To create a public link, set `share=True` in `launch()`.
Startup time: 45.7s (prepare environment: 32.9s, launcher: 0.2s, import torch: 2.5s, import gradio: 0.7s, setup paths: 0.6s, other imports: 0.6s, load scripts: 7.6s, create ui: 0.4s).
Loading model from checkpoint.
Loading safetensors...
Pred and size are epsilon and 768, using config: C:\Users\User\stable-diffusion-webui\extensions\sd_dreambooth_extension\dreambooth\..\configs\v1-training-default.yaml
v1 model loaded.
Trying to load: C:\Users\User\stable-diffusion-webui\extensions\sd_dreambooth_extension\dreambooth\..\configs\v1-training-default.yaml
Converting unet...
Exception setting up output: Error(s) in loading state_dict for UNet2DConditionModel:
        Missing key(s) in state_dict: "down_blocks.0.attentions.0.norm.weight", "down_blocks.0.attentions.0.norm.bias", "down_blocks.0.attentions.0.proj_in.weight", "down_blocks.0.attentions.0.proj_in.bias", "down_blocks.0.attentions.0.transformer_blocks.0.norm1.weight", "down_blocks.0.attentions.0.transformer_blocks.0.norm1.bias", "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_q.weight", "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_k.weight", "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_v.weight", "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_out.0.weight", "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_out.0.bias", "down_blocks.0.attentions.0.transformer_blocks.0.norm2.weight", "down_blocks.0.attentions.0.transformer_blocks.0.norm2.bias", "down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_q.weight", "down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_k.weight", "down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_v.weight", "down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_out.0.weight", "down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_out.0.bias", "down_blocks.0.attentions.0.transformer_blocks.0.norm3.weight", "down_blocks.0.attentions.0.transformer_blocks.0.norm3.bias", "down_blocks.0.attentions.0.transformer_blocks.0.ff.net.0.proj.weight", "down_blocks.0.attentions.0.transformer_blocks.0.ff.net.0.proj.bias", "down_blocks.0.attentions.0.transformer_blocks.0.ff.net.2.weight", "down_blocks.0.attentions.0.transformer_blocks.0.ff.net.2.bias", "down_blocks.0.attentions.0.proj_out.weight", "down_blocks.0.attentions.0.proj_out.bias", "down_blocks.0.attentions.1.norm.weight", "down_blocks.0.attentions.1.norm.bias", "down_blocks.0.attentions.1.proj_in.weight", "down_blocks.0.attentions.1.proj_in.bias", "down_blocks.0.attentions.1.transformer_blocks.0.norm1.weight", "down_blocks.0.attentions.1.transformer_blocks.0.norm1.bias", "mid_block.attentions.0.transformer_blocks.8.attn1.to_k.weight", "mid_block.attentions.0.transformer_blocks.8.attn1.to_out.0.bias", "mid_block.attentions.0.transformer_blocks.8.attn1.to_out.0.weight", "mid_block.attentions.0.transformer_blocks.8.attn1.to_q.weight", "mid_block.attentions.0.transformer_blocks.8.attn1.to_v.weight", "mid_block.attentions.0.transformer_blocks.8.attn2.to_k.weight", "mid_block.attentions.0.transformer_blocks.8.attn2.to_out.0.bias", "mid_block.attentions.0.transformer_blocks.8.attn2.to_out.0.weight", "mid_block.attentions.0.transformer_blocks.8.attn2.to_q.weight", "mid_block.attentions.0.transformer_blocks.8.attn2.to_v.weight", "mid_block.attentions.0.transformer_blocks.8.ff.net.0.proj.bias", "mid_block.attentions.0.transformer_blocks.8.ff.net.0.proj.weight", "mid_block.attentions.0.transformer_blocks.8.ff.net.2.bias", "mid_block.attentions.0.transformer_blocks.8.ff.net.2.weight", "mid_block.attentions.0.transformer_blocks.8.norm1.bias", "mid_block.attentions.0.transformer_blocks.8.norm1.weight", "mid_block.attentions.0.transformer_blocks.8.norm2.bias", "mid_block.attentions.0.transformer_blocks.8.norm2.weight", "mid_block.attentions.0.transformer_blocks.8.norm3.bias", "mid_block.attentions.0.transformer_blocks.8.norm3.weight", "mid_block.attentions.0.transformer_blocks.9.attn1.to_k.weight", "mid_block.attentions.0.transformer_blocks.9.attn1.to_out.0.bias", "mid_block.attentions.0.transformer_blocks.9.attn1.to_out.0.weight", "mid_block.attentions.0.transformer_blocks.9.attn1.to_q.weight", "mid_block.attentions.0.transformer_blocks.9.attn1.to_v.weight", "mid_block.attentions.0.transformer_blocks.9.attn2.to_k.weight", "mid_block.attentions.0.transformer_blocks.9.attn2.to_out.0.bias", "mid_block.attentions.0.transformer_blocks.9.attn2.to_out.0.weight", "mid_block.attentions.0.transformer_blocks.9.attn2.to_q.weight", "mid_block.attentions.0.transformer_blocks.9.attn2.to_v.weight", "mid_block.attentions.0.transformer_blocks.9.ff.net.0.proj.bias", "mid_block.attentions.0.transformer_blocks.9.ff.net.0.proj.weight", "mid_block.attentions.0.transformer_blocks.9.ff.net.2.bias", "mid_block.attentions.0.transformer_blocks.9.ff.net.2.weight", "mid_block.attentions.0.transformer_blocks.9.norm1.bias", "mid_block.attentions.0.transformer_blocks.9.norm1.weight", "mid_block.attentions.0.transformer_blocks.9.norm2.bias", "mid_block.attentions.0.transformer_blocks.9.norm2.weight", "mid_block.attentions.0.transformer_blocks.9.norm3.bias", "mid_block.attentions.0.transformer_blocks.9.norm3.weight".
        size mismatch for down_blocks.1.attentions.0.proj_in.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([640, 640, 1, 1]).
        size mismatch for down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([640, 2048]) from checkpoint, the shape in current model is torch.Size([640, 768]).
        size mismatch for down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([640, 2048]) from checkpoint, the shape in current model is torch.Size([640, 768]).
        size mismatch for down_blocks.1.attentions.0.proj_out.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([640, 640, 1, 1]).
        size mismatch for down_blocks.1.attentions.1.proj_in.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([640, 640, 1, 1]).
        size mismatch for down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([640, 2048]) from checkpoint, the shape in current model is torch.Size([640, 768]).
        size mismatch for down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([640, 2048]) from checkpoint, the shape in current model is torch.Size([640, 768]).
        size mismatch for down_blocks.1.attentions.1.proj_out.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([640, 640, 1, 1]).
        size mismatch for down_blocks.2.attentions.0.proj_in.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
        size mismatch for down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
        size mismatch for down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
        size mismatch for down_blocks.2.attentions.0.proj_out.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
        size mismatch for down_blocks.2.attentions.1.proj_in.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
        size mismatch for down_blocks.2.attentions.1.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
        size mismatch for down_blocks.2.attentions.1.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
        size mismatch for down_blocks.2.attentions.1.proj_out.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
        size mismatch for up_blocks.0.resnets.2.norm1.weight: copying a param with shape torch.Size([1920]) from checkpoint, the shape in current model is torch.Size([2560]).
        size mismatch for up_blocks.0.resnets.2.norm1.bias: copying a param with shape torch.Size([1920]) from checkpoint, the shape in current model is torch.Size([2560]).
        size mismatch for up_blocks.0.resnets.2.conv1.weight: copying a param with shape torch.Size([1280, 1920, 3, 3]) from checkpoint, the shape in current model is torch.Size([1280, 2560, 3, 3]).
        size mismatch for up_blocks.0.resnets.2.conv_shortcut.weight: copying a param with shape torch.Size([1280, 1920, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 2560, 1, 1]).
        size mismatch for up_blocks.1.attentions.0.norm.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.0.norm.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.0.proj_in.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
        size mismatch for up_blocks.1.attentions.0.proj_in.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.norm1.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.norm1.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_q.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_k.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_v.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_out.0.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_out.0.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.norm2.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.norm2.bias: copying a param with "mid_block.attentions.0.transformer_blocks.9.ff.net.0.proj.weight", "mid_block.attentions.0.transformer_blocks.9.ff.net.2.bias", "mid_block.attentions.0.transformer_blocks.9.ff.net.2.weight", "mid_block.attentions.0.transformer_blocks.9.norm1.bias", "mid_block.attentions.0.transformer_blocks.9.norm1.weight", "mid_block.attentions.0.transformer_blocks.9.norm2.bias", "mid_block.attentions.0.transformer_blocks.9.norm2.weight", "mid_block.attentions.0.transformer_blocks.9.norm3.bias", "mid_block.attentions.0.transformer_blocks.9.norm3.weight".
        size mismatch for down_blocks.1.attentions.0.proj_in.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([640, 640, 1, 1]).
        size mismatch for down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([640, 2048]) from checkpoint, the shape in current model is torch.Size([640, 768]).
        size mismatch for down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([640, 2048]) from checkpoint, the shape in current model is torch.Size([640, 768]).
        size mismatch for down_blocks.1.attentions.0.proj_out.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([640, 640, 1, 1]).
        size mismatch for down_blocks.1.attentions.1.proj_in.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([640, 640, 1, 1]).
        size mismatch for down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([640, 2048]) from checkpoint, the shape in current model is torch.Size([640, 768]).
        size mismatch for down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([640, 2048]) from checkpoint, the shape in current model is torch.Size([640, 768]).
        size mismatch for down_blocks.1.attentions.1.proj_out.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([640, 640, 1, 1]).
        size mismatch for down_blocks.2.attentions.0.proj_in.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
        size mismatch for down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
        size mismatch for down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
        size mismatch for down_blocks.2.attentions.0.proj_out.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
        size mismatch for down_blocks.2.attentions.1.proj_in.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
        size mismatch for down_blocks.2.attentions.1.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
        size mismatch for down_blocks.2.attentions.1.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
        size mismatch for down_blocks.2.attentions.1.proj_out.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
        size mismatch for up_blocks.0.resnets.2.norm1.weight: copying a param with shape torch.Size([1920]) from checkpoint, the shape in current model is torch.Size([2560]).
        size mismatch for up_blocks.0.resnets.2.norm1.bias: copying a param with shape torch.Size([1920]) from checkpoint, the shape in current model is torch.Size([2560]).
        size mismatch for up_blocks.0.resnets.2.conv1.weight: copying a param with shape torch.Size([1280, 1920, 3, 3]) from checkpoint, the shape in current model is torch.Size([1280, 2560, 3, 3]).
        size mismatch for up_blocks.0.resnets.2.conv_shortcut.weight: copying a param with shape torch.Size([1280, 1920, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 2560, 1, 1]).
        size mismatch for up_blocks.1.attentions.0.norm.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.0.norm.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.0.proj_in.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
        size mismatch for up_blocks.1.attentions.0.proj_in.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.norm1.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.norm1.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_q.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_k.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_v.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_out.0.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn1.to_out.0.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.norm2.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.norm2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_q.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([640, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([640, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_out.0.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_out.0.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.norm3.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.norm3.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.ff.net.0.proj.weight: copying a param with shape torch.Size([5120, 640]) from checkpoint, the shape in current model is torch.Size([10240, 1280]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.ff.net.0.proj.bias: copying a param with shape torch.Size([5120]) from checkpoint, the shape in current model is torch.Size([10240]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.ff.net.2.weight: copying a param with shape torch.Size([640, 2560]) from checkpoint, the shape in current model is torch.Size([1280, 5120]).
        size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.ff.net.2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.0.proj_out.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
        size mismatch for up_blocks.1.attentions.0.proj_out.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.1.norm.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.1.norm.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.1.proj_in.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
        size mismatch for up_blocks.1.attentions.1.proj_in.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.norm1.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.norm1.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_q.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
        size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_k.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
        size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_v.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
        size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_out.0.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
        size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn1.to_out.0.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.norm2.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.norm2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_q.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
        size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([640, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
        size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([640, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
        size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_out.0.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
        size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_out.0.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.norm3.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.norm3.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.ff.net.0.proj.weight: copying a param with shape torch.Size([5120, 640]) from checkpoint, the shape in current model is torch.Size([10240, 1280]).
        size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.ff.net.0.proj.bias: copying a param with shape torch.Size([5120]) from checkpoint, the shape in current model is torch.Size([10240]).
        size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.ff.net.2.weight: copying a param with shape torch.Size([640, 2560]) from checkpoint, the shape in current model is torch.Size([1280, 5120]).
        size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.ff.net.2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.1.proj_out.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
        size mismatch for up_blocks.1.attentions.1.proj_out.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.2.norm.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.2.norm.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.2.proj_in.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
        size mismatch for up_blocks.1.attentions.2.proj_in.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.norm1.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.norm1.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_q.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
        size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_k.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
        size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_v.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
        size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_out.0.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
        size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn1.to_out.0.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.norm2.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.norm2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_q.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
        size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([640, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
        size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([640, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
        size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_out.0.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
        size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_out.0.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.norm3.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.norm3.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.ff.net.0.proj.weight: copying a param with shape torch.Size([5120, 640]) from checkpoint, the shape in current model is torch.Size([10240, 1280]).
        size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.ff.net.0.proj.bias: copying a param with shape torch.Size([5120]) from checkpoint, the shape in current model is torch.Size([10240]).
        size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.ff.net.2.weight: copying a param with shape torch.Size([640, 2560]) from checkpoint, the shape in current model is torch.Size([1280, 5120]).
        size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.ff.net.2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.attentions.2.proj_out.weight: copying a param with shape torch.Size([640, 640]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
        size mismatch for up_blocks.1.attentions.2.proj_out.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.resnets.0.norm1.weight: copying a param with shape torch.Size([1920]) from checkpoint, the shape in current model is torch.Size([2560]).
        size mismatch for up_blocks.1.resnets.0.norm1.bias: copying a param with shape torch.Size([1920]) from checkpoint, the shape in current model is torch.Size([2560]).
        size mismatch for up_blocks.1.resnets.0.conv1.weight: copying a param with shape torch.Size([640, 1920, 3, 3]) from checkpoint, the shape in current model is torch.Size([1280, 2560, 3, 3]).
        size mismatch for up_blocks.1.resnets.0.conv1.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.resnets.0.time_emb_proj.weight: copying a param with shape torch.Size([640, 1280]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
        size mismatch for up_blocks.1.resnets.0.time_emb_proj.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.resnets.0.norm2.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.resnets.0.norm2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.resnets.0.conv2.weight: copying a param with shape torch.Size([640, 640, 3, 3]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 3, 3]).
        size mismatch for up_blocks.1.resnets.0.conv2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.resnets.0.conv_shortcut.weight: copying a param with shape torch.Size([640, 1920, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 2560, 1, 1]).
        size mismatch for up_blocks.1.resnets.0.conv_shortcut.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.resnets.1.norm1.weight: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([2560]).
        size mismatch for up_blocks.1.resnets.1.norm1.bias: copying a param with shape torch.Size([1280]) from checkpoint, the shape in current model is torch.Size([2560]).
        size mismatch for up_blocks.1.resnets.1.conv1.weight: copying a param with shape torch.Size([640, 1280, 3, 3]) from checkpoint, the shape in current model is torch.Size([1280, 2560, 3, 3]).
        size mismatch for up_blocks.1.resnets.1.conv1.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.resnets.1.time_emb_proj.weight: copying a param with shape torch.Size([640, 1280]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
        size mismatch for up_blocks.1.resnets.1.time_emb_proj.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.resnets.1.norm2.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.resnets.1.norm2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.resnets.1.conv2.weight: copying a param with shape torch.Size([640, 640, 3, 3]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 3, 3]).
        size mismatch for up_blocks.1.resnets.1.conv2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.resnets.1.conv_shortcut.weight: copying a param with shape torch.Size([640, 1280, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 2560, 1, 1]).
        size mismatch for up_blocks.1.resnets.1.conv_shortcut.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.resnets.2.norm1.weight: copying a param with shape torch.Size([960]) from checkpoint, the shape in current model is torch.Size([1920]).
        size mismatch for up_blocks.1.resnets.2.norm1.bias: copying a param with shape torch.Size([960]) from checkpoint, the shape in current model is torch.Size([1920]).
        size mismatch for up_blocks.1.resnets.2.conv1.weight: copying a param with shape torch.Size([640, 960, 3, 3]) from checkpoint, the shape in current model is torch.Size([1280, 1920, 3, 3]).
        size mismatch for up_blocks.1.resnets.2.conv1.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.resnets.2.time_emb_proj.weight: copying a param with shape torch.Size([640, 1280]) from checkpoint, the shape in current model is torch.Size([1280, 1280]).
        size mismatch for up_blocks.1.resnets.2.time_emb_proj.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.resnets.2.norm2.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.resnets.2.norm2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.resnets.2.conv2.weight: copying a param with shape torch.Size([640, 640, 3, 3]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 3, 3]).
        size mismatch for up_blocks.1.resnets.2.conv2.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.resnets.2.conv_shortcut.weight: copying a param with shape torch.Size([640, 960, 1, 1]) from checkpoint, the shape in current model is torch.Size([1280, 1920, 1, 1]).
        size mismatch for up_blocks.1.resnets.2.conv_shortcut.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.1.upsamplers.0.conv.weight: copying a param with shape torch.Size([640, 640, 3, 3]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 3, 3]).
        size mismatch for up_blocks.1.upsamplers.0.conv.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.2.resnets.0.norm1.weight: copying a param with shape torch.Size([960]) from checkpoint, the shape in current model is torch.Size([1920]).
        size mismatch for up_blocks.2.resnets.0.norm1.bias: copying a param with shape torch.Size([960]) from checkpoint, the shape in current model is torch.Size([1920]).
        size mismatch for up_blocks.2.resnets.0.conv1.weight: copying a param with shape torch.Size([320, 960, 3, 3]) from checkpoint, the shape in current model is torch.Size([640, 1920, 3, 3]).
        size mismatch for up_blocks.2.resnets.0.conv1.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
        size mismatch for up_blocks.2.resnets.0.time_emb_proj.weight: copying a param with shape torch.Size([320, 1280]) from checkpoint, the shape in current model is torch.Size([640, 1280]).
        size mismatch for up_blocks.2.resnets.0.time_emb_proj.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
        size mismatch for up_blocks.2.resnets.0.norm2.weight: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
        size mismatch for up_blocks.2.resnets.0.norm2.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
        size mismatch for up_blocks.2.resnets.0.conv2.weight: copying a param with shape torch.Size([320, 320, 3, 3]) from checkpoint, the shape in current model is torch.Size([640, 640, 3, 3]).
        size mismatch for up_blocks.2.resnets.0.conv2.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
        size mismatch for up_blocks.2.resnets.0.conv_shortcut.weight: copying a param with shape torch.Size([320, 960, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 1920, 1, 1]).
        size mismatch for up_blocks.2.resnets.0.conv_shortcut.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
        size mismatch for up_blocks.2.resnets.1.norm1.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.2.resnets.1.norm1.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([1280]).
        size mismatch for up_blocks.2.resnets.1.conv1.weight: copying a param with shape torch.Size([320, 640, 3, 3]) from checkpoint, the shape in current model is torch.Size([640, 1280, 3, 3]).
        size mismatch for up_blocks.2.resnets.1.conv1.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
        size mismatch for up_blocks.2.resnets.1.time_emb_proj.weight: copying a param with shape torch.Size([320, 1280]) from checkpoint, the shape in current model is torch.Size([640, 1280]).
        size mismatch for up_blocks.2.resnets.1.time_emb_proj.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
        size mismatch for up_blocks.2.resnets.1.norm2.weight: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
        size mismatch for up_blocks.2.resnets.1.norm2.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
        size mismatch for up_blocks.2.resnets.1.conv2.weight: copying a param with shape torch.Size([320, 320, 3, 3]) from checkpoint, the shape in current model is torch.Size([640, 640, 3, 3]).
        size mismatch for up_blocks.2.resnets.1.conv2.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
        size mismatch for up_blocks.2.resnets.1.conv_shortcut.weight: copying a param with shape torch.Size([320, 640, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 1280, 1, 1]).
        size mismatch for up_blocks.2.resnets.1.conv_shortcut.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
        size mismatch for up_blocks.2.resnets.2.norm1.weight: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([960]).
        size mismatch for up_blocks.2.resnets.2.norm1.bias: copying a param with shape torch.Size([640]) from checkpoint, the shape in current model is torch.Size([960]).
        size mismatch for up_blocks.2.resnets.2.conv1.weight: copying a param with shape torch.Size([320, 640, 3, 3]) from checkpoint, the shape in current model is torch.Size([640, 960, 3, 3]).
        size mismatch for up_blocks.2.resnets.2.conv1.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
        size mismatch for up_blocks.2.resnets.2.time_emb_proj.weight: copying a param with shape torch.Size([320, 1280]) from checkpoint, the shape in current model is torch.Size([640, 1280]).
        size mismatch for up_blocks.2.resnets.2.time_emb_proj.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
        size mismatch for up_blocks.2.resnets.2.norm2.weight: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
        size mismatch for up_blocks.2.resnets.2.norm2.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
        size mismatch for up_blocks.2.resnets.2.conv2.weight: copying a param with shape torch.Size([320, 320, 3, 3]) from checkpoint, the shape in current model is torch.Size([640, 640, 3, 3]).
        size mismatch for up_blocks.2.resnets.2.conv2.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
        size mismatch for up_blocks.2.resnets.2.conv_shortcut.weight: copying a param with shape torch.Size([320, 640, 1, 1]) from checkpoint, the shape in current model is torch.Size([640, 960, 1, 1]).
        size mismatch for up_blocks.2.resnets.2.conv_shortcut.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([640]).
        size mismatch for mid_block.attentions.0.proj_in.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
        size mismatch for mid_block.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
        size mismatch for mid_block.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 2048]) from checkpoint, the shape in current model is torch.Size([1280, 768]).
        size mismatch for mid_block.attentions.0.proj_out.weight: copying a param with shape torch.Size([1280, 1280]) from checkpoint, the shape in current model is torch.Size([1280, 1280, 1, 1]).
Missing model directory, removing model: C:\Users\User\stable-diffusion-webui\models\dreambooth\TrainTestSDXL\working\unet
Restored system models.
Duration: 00:00:06
Exception loading config: [Errno 2] No such file or directory: 'C:\\Users\\User\\stable-diffusion-webui\\models\\dreambooth\\TrainTestSDXL\\db_config.json'
Traceback (most recent call last):
  File "C:\Users\User\stable-diffusion-webui\extensions\sd_dreambooth_extension\dreambooth\dataclasses\db_config.py", line 410, in from_file
    with open(config_file, 'r') as openfile:
FileNotFoundError: [Errno 2] No such file or directory: 'C:\\Users\\User\\stable-diffusion-webui\\models\\dreambooth\\TrainTestSDXL\\db_config.json'
Can't load config!
Traceback (most recent call last):
  File "C:\Users\User\stable-diffusion-webui\venv\lib\site-packages\gradio\routes.py", line 422, in run_predict
    output = await app.get_blocks().process_api(
  File "C:\Users\User\stable-diffusion-webui\venv\lib\site-packages\gradio\blocks.py", line 1326, in process_api
    data = self.postprocess_data(fn_index, result["prediction"], state)
  File "C:\Users\User\stable-diffusion-webui\venv\lib\site-packages\gradio\blocks.py", line 1229, in postprocess_data
    self.validate_outputs(fn_index, predictions)  # type: ignore
  File "C:\Users\User\stable-diffusion-webui\venv\lib\site-packages\gradio\blocks.py", line 1204, in validate_outputs
    raise ValueError(
ValueError: An event handler (load_model_params) didn't receive enough output values (needed: 10, received: 7).
Wanted outputs:
    [html, html, html, html, html, html, html, dropdown, dropdown, html]
Received outputs:
    ["", "", "", "", "", {'visible': True, 'choices': [], 'value': '', '__type__': 'generic_update'}, "Error loading model params: 'TrainTestSDXL'."]

is there anything i am missing? is it not possible to train SDXL models with dreambooth/automatic1111 ?

thanks for sharing some light into this issue



### Additional information

_No response_
78Alpha commented 1 year ago

As far as I know, the dreambooth extension does not support SDXL at all. Only Kohya_ss was updated to support it. Also, this would be better suited for the Dreambooth extension issues page.